There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.
), but the art of data analysis is about choosing and using multiple tools.
In the words of Chatfield .
students typically know the technical details of regressin for example, but not necessarily when and how to apply it.
This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies.
Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address.
For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks ) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.
About the Author: Frank E.
Harrell, Jr.
is Professor of Biostatistics and Chair, Department of Biostatistics, Vanderbilt University School of Medicine, Nashville.
He has developed numerous methods for predictive modeling, quantifying predictive accuracy and model validation and has published numerous predictive models and articles on applied statistics, medical research and clinical trials.
He is on the editorial board for several biomedical and methodologic journals.
He is a Fellow of the American Statistical Association (ASA) and a consultant to the U.
Food and Drug Administration and to the pharmaceutical industry.
He teaches a graduate course in regression modeling strategies and a course in biostatistics for medical researchers.
In 2014 he was chosen to receive the WJ Dixon Award for Excellence in Statistical Consulting by the ASA.
Springer Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis/frank e. harrell jr
